
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase     :	Tuple       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	Tuple       			=  {
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""git_vision_model"""
  def __init__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Dict=768     ,				UpperCamelCase__     :					Dict=3072     ,				UpperCamelCase__     :					Union[str, Any]=12     ,				UpperCamelCase__     :					Dict=12     ,				UpperCamelCase__     :					int=3     ,				UpperCamelCase__     :					Union[str, Any]=224     ,				UpperCamelCase__     :					Tuple=16     ,				UpperCamelCase__     :					Tuple="quick_gelu"     ,				UpperCamelCase__     :					int=1E-5     ,				UpperCamelCase__     :					Dict=0.0     ,				UpperCamelCase__     :					int=0.02     ,				**UpperCamelCase__     :					int     ,				)     ->      int:







    """simple docstring"""






    super().__init__(**UpperCamelCase__       )

    __magic_name__								=			hidden_size
    __magic_name__								=			intermediate_size
    __magic_name__								=			num_hidden_layers
    __magic_name__								=			num_attention_heads
    __magic_name__								=			num_channels
    __magic_name__								=			patch_size
    __magic_name__								=			image_size
    __magic_name__								=			initializer_range
    __magic_name__								=			attention_dropout
    __magic_name__								=			layer_norm_eps
    __magic_name__								=			hidden_act





  @classmethod
  def 				_lowercase				(					cls     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, os.PathLike]     ,				**UpperCamelCase__     :					List[str]       )     ->      "PretrainedConfig":







    """simple docstring"""






    cls._set_token_in_kwargs(UpperCamelCase__       )

    __magic_name__	,       __magic_name__								=			cls.get_config_dict(UpperCamelCase__     ,				**UpperCamelCase__       )

    # get the vision config dict if we are loading from GITConfig
    if config_dict.get("""model_type"""       ) == "git":
      __magic_name__								=			config_dict["""vision_config"""]

    if "model_type" in config_dict and hasattr(cls     ,				"""model_type"""       ) and config_dict["model_type"] != cls.model_type:
      logger.warning(
          F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
          F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''       )

    return cls.from_dict(UpperCamelCase__     ,				**UpperCamelCase__       )







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""git"""
  def __init__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple=None     ,				UpperCamelCase__     :					Union[str, Any]=3_0522     ,				UpperCamelCase__     :					str=768     ,				UpperCamelCase__     :					Dict=6     ,				UpperCamelCase__     :					List[Any]=12     ,				UpperCamelCase__     :					int=3072     ,				UpperCamelCase__     :					Any="gelu"     ,				UpperCamelCase__     :					Dict=0.1     ,				UpperCamelCase__     :					List[str]=0.1     ,				UpperCamelCase__     :					Optional[int]=1024     ,				UpperCamelCase__     :					Union[str, Any]=0.02     ,				UpperCamelCase__     :					List[Any]=1E-12     ,				UpperCamelCase__     :					int=0     ,				UpperCamelCase__     :					Dict="absolute"     ,				UpperCamelCase__     :					Dict=True     ,				UpperCamelCase__     :					Any=False     ,				UpperCamelCase__     :					List[str]=101     ,				UpperCamelCase__     :					Any=102     ,				UpperCamelCase__     :					int=None     ,				**UpperCamelCase__     :					Optional[Any]     ,				)     ->      List[str]:







    """simple docstring"""






    super().__init__(bos_token_id=UpperCamelCase__     ,				eos_token_id=UpperCamelCase__     ,				pad_token_id=UpperCamelCase__     ,				**UpperCamelCase__       )

    if vision_config is None:
      __magic_name__								=			{}
      logger.info("""vision_config is None. initializing the GitVisionConfig with default values."""       )

    __magic_name__								=			GitVisionConfig(**UpperCamelCase__       )
    __magic_name__								=			vocab_size
    __magic_name__								=			hidden_size
    __magic_name__								=			num_hidden_layers
    __magic_name__								=			num_attention_heads
    __magic_name__								=			hidden_act
    __magic_name__								=			intermediate_size
    __magic_name__								=			hidden_dropout_prob
    __magic_name__								=			attention_probs_dropout_prob
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			initializer_range
    __magic_name__								=			layer_norm_eps
    __magic_name__								=			position_embedding_type
    __magic_name__								=			use_cache
    __magic_name__								=			tie_word_embeddings
    __magic_name__								=			num_image_with_embedding

    __magic_name__								=			bos_token_id
    __magic_name__								=			eos_token_id





  def 				_lowercase				(					self     :					int       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			copy.deepcopy(self.__dict__       )
    __magic_name__								=			self.vision_config.to_dict()
    __magic_name__								=			self.__class__.model_type
    return output



import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  if gpta_config_file == "":
    __magic_name__								=			GPTaConfig()
  else:
    __magic_name__								=			GPTaConfig.from_json_file(A_   )
  __magic_name__								=			GPTaModel(A_   )

  # Load weights from numpy
  load_tf_weights_in_gpta(A_,	A_,	A_   )

  # Save pytorch-model
  __magic_name__								=			pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  __magic_name__								=			pytorch_dump_folder_path + """/""" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''   )
  torch.save(model.state_dict(),	A_   )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''   )
  with open(A_,	"""w""",	encoding="""utf-8"""   ) as f:
    f.write(config.to_json_string()   )


if __name__ == "__main__":
    __lowerCAmelCase     :	List[str]       			=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--gpt2_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained OpenAI model. \n'
            'This specifies the model architecture.'
        ),
    )
    __lowerCAmelCase     :	Tuple       			=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)

